A transplantation of subject-independent model in cross-platform BCI

被引：4

作者：

Zhao, Yawei ^{[1
]}

Wang, Zhongpeng ^{[1
]}

Zhang, Zhen ^{[2
]}

Liu, Jing ^{[1
]}

Chen, Long ^{[1
]}

Qi, Hongzhi ^{[1
]}

Jiao, Xuejun ^{[2
]}

He, Feng ^{[1
]}

Zhou, Peng ^{[1
]}

Ming, Dong ^{[1
]}

机构：

[1] Tianjin Univ, Coll Precis Instruments & Optoelect Engn, Dept Biomed Engn, Tianjin 2, Peoples R China

[2] China Astronaut Res & Training Ctr, Natl Key Lab Human Factors Engn, Beijing, Peoples R China

来源：

INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS | 2018年 / 9卷 / 06期

基金：

中国国家自然科学基金;

关键词：

Subject-independent BCI; ERP; P300-Speller; Cross-platform; LDA; BRAIN-COMPUTER INTERFACES; CLASSIFICATION; ENSEMBLE;

D O I：

10.1007/s13042-016-0620-1

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

With the development of wearable technology, portable wireless systems have been used gradually for collecting electroencephalogram (EEG) signals. However, the introduction of portable collection devices always means a descent in signal-to-noise ratio (SNR) of EEG. Subject-independent brain-computer interface (BCI) avoids conventional calibration procedure for new users. However, whether subject-independent model can be used in cross-platform BCI has not been discussed so far. This paper transplanted the subject-independent model from a high-SNR platform to a lower one for recognition in P300-Speller. After comparing their EEG features elicited from diverse collection platforms, a model adjustment strategy was proposed to increase recognition accuracy. By model adjustment, the average accuracy was 85.00% in online spell experiments. The results indicate it is feasible for subject-independent model transplantation, especially after model adjustment strategy. It provides technology supported for further development of cross-platform BCI.

引用

页码：959 / 967

页数：9

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